PurposeTo describe training variations across the annual cycle in Olympic and World Champion endurance athletes, and determine whether these athletes used tapering strategies in line with recommendations in the literature.MethodsEleven elite XC skiers and biathletes (4 male; 28±1 yr, 85±5 mL. min−1. kg−1 , 7 female, 25±4 yr, 73±3 mL. min−1. kg−1 ) reported one year of day-to-day training leading up to the most successful competition of their career. Training data were divided into periodization and peaking phases and distributed into training forms, intensity zones and endurance activity forms.ResultsAthletes trained ∼800 h/500 sessions.year−1, including ∼500 h. year−1 of sport-specific training. Ninety-four percent of all training was executed as aerobic endurance training. Of this, ∼90% was low intensity training (LIT, below the first lactate threshold) and 10% high intensity training (HIT, above the first lactate threshold) by time. Categorically, 23% of training sessions were characterized as HIT with primary portions executed at or above the first lactate turn point. Training volume and specificity distribution conformed to a traditional periodization model, but absolute volume of HIT remained stable across phases. However, HIT training patterns tended to become more polarized in the competition phase. Training volume, frequency and intensity remained unchanged from pre-peaking to peaking period, but there was a 32±15% (P<.01) volume reduction from the preparation period to peaking phase.ConclusionsThe annual training data for these Olympic and World champion XC skiers and biathletes conforms to previously reported training patterns of elite endurance athletes. During the competition phase, training became more sport-specific, with 92% performed as XC skiing. However, they did not follow suggested tapering practice derived from short-term experimental studies. Only three out of 11 athletes took a rest day during the final 5 days prior to their most successful competition.

Endurance athletes are at increased risk of relative energy deficiency associated with metabolic perturbation and impaired health. We aimed to estimate and compare within-day energy balance in male athletes with suppressed and normal resting metabolic rate (RMR) and explore whether within-day energy deficiency is associated with endocrine markers of energy deficiency. A total of 31 male cyclists, triathletes, and long-distance runners recruited from regional competitive sports clubs were included. The protocol comprised measurements of RMR by ventilated hood and energy intake and energy expenditure to predict RMR (measured RMR/predicted RMR), energy availability, 24-hr energy balance and within-day energy balance in 1-hr intervals, assessment of body composition by dual-energy X-ray absorptiometry, and blood plasma analysis. Subjects were categorized as having suppressed (RMR < 0.90, n = 20) or normal (RMR > 0.90, n = 11) RMR. Despite there being no observed differences in 24-hr energy balance or energy availability between the groups, subjects with suppressed RMR spent more time in an energy deficit exceeding 400 kcal (20.9 [18.8-21.8] hr vs. 10.8 [2.5-16.4], p = .023) and had larger single-hour energy deficits compared with subjects with normal RMR (3,265 ± 1,963 kcal vs. -1,340 ± 2,439, p = .023). Larger single-hour energy deficits were associated with higher cortisol levels (r = -.499, p = .004) and a lower testosterone:cortisol ratio (r = .431, p = .015), but no associations with triiodothyronine or fasting blood glucose were observed. In conclusion, within-day energy deficiency was associated with suppressed RMR and catabolic markers in male endurance athletes.

From heart rate data to training quantification: a comparison of three methods of training intensity analysisThe proportion of training in zone 1, zone 2 and zone 3 was quantified using total training time or frequency of sessions, and simple conversion factors across different methods were calculated. Results: Comparing the TIZ and SG/TIZ methods, 96.1 and 95.5 % respectively of total training time was spent in zone 1 (P < .001), with 2.9/3.6 and 1.1/0.8 % in zones 2/3 (P < .001). Using SG, this corresponded to 86.6 % zone 1 and 11.1/2.4 % zone 2/3 sessions. Estimated conversion factors from TIZ or SG/TIZ to SG and vice versa, were 0.9/1.1 respectively in the low intensity training (LIT) range (zone 1), and 3.0/0.33 in the high intensity training (HIT) range (zone 2 & 3). Conclusions: This study provides a direct comparison and practical conversion factors across studies employing different methods of TID quantification associated with the most common HR based analysis methods.

. Purpose: This study aimed to compare the effects of three different high-intensity training (HIT) models, balanced for total load but differing in training plan progression, on endurance adaptations. Methods: Sixty-three cyclists (peak oxygen uptake (V O 2peak ) 61.3 T 5.8 mLIkg j1 Imin j1 ) were randomized to three training groups and instructed to follow a 12-wk training program consisting of 24 interval sessions, a high volume of low-intensity training, and laboratory testing. The increasing HIT group (n = 23) performed interval training as 4 Â 16 min in weeks 1-4, 4 Â 8 min in weeks 5-8, and 4 Â 4 min in weeks 9-12. The decreasing HIT group (n = 20) performed interval sessions in the opposite mesocycle order as the increasing HIT group, and the mixed HIT group (n = 20) performed the interval prescriptions in a mixed distribution in all mesocycles. Interval sessions were prescribed as maximal session efforts and executed at mean values 4.7, 9.2, and 12.7 mmolIL j1 blood lactate in 4 Â 16-, 4 Â 8-, and 4 Â 4-min sessions, respectively (P G 0.001). Pre-and postintervention, cyclists were tested for mean power during a 40-min all-out trial, peak power output during incremental testing to exhaustion, V O 2peak , and power at 4 mmolIL j1 lactate. Results: All groups improved 5%-10% in mean power during a 40-min all-out trial, peak power output, and V O 2peak postintervention (P G 0.05), but no adaptation differences emerged among the three training groups (P 9 0.05). Further, an individual response analysis indicated similar likelihood of large, moderate, or nonresponses, respectively, in response to each training group (P 9 0.05). Conclusions: This study suggests that organizing different interval sessions in a specific periodized mesocycle order or in a mixed distribution during a 12-wk training period has little or no effect on training adaptation when the overall training load is the same.